A Wager on the Turing Test: Why I Think I Will Win

Will Ray Kurzweil’s predictions come true? He’s putting his money where his mouth is. Here’s why he thinks he will win a bet on the future of artificial intelligence. The wager: an AI that passes the Turing Test by 2029.

Published April 9, 2002 on KurzweilAI.net. Click here to read an explanation of the bet and its background, with rules and definitions. Click here to read Mitch Kapor’s response. Also see Ray Kurzweil’s final word on why he will win.

The Significance of the Turing Test. The implicit, and in my view brilliant, insight in Turing’s eponymous test is the ability of written human language to represent human-level thinking. The basis of the Turing test is that if the human Turing test judge is competent, then an entity requires human-level intelligence in order to pass the test. The human judge is free to probe each candidate with regard to their understanding of basic human knowledge, current events, aspects of the candidate’s personal history and experiences, as well as their subjective experiences, all expressed through written language. As humans jump from one concept and one domain to the next, it is possible to quickly touch upon all human knowledge, on all aspects of human, well, humanness.

To the extent that the “AI” chooses to reveal its “history” during the interview with the Turing Test judge (note that none of the contestants are required to reveal their histories), the AI will need to use a fictional human history because “it” will not be in a position to be honest about its origins as a machine intelligence and pass the test. (By the way, I put the word “it” in quotes because it is my view that once an AI does indeed pass the Turing Test, we may very well consider “it” to be a “he” or a “she.”) This makes the task of the machines somewhat more difficult than that of the human foils because the humans can use their own history. As fiction writers will attest, presenting a totally convincing human history that is credible and tracks coherently is a challenging task that most humans are unable to accomplish successfully. However, some humans are capable of doing this, and it will be a necessary task for a machine to pass the Turing test.

There are many contemporary examples of computers passing “narrow” forms of the Turing test, that is, demonstrating human-level intelligence in specific domains. For example, Gary Kasparov, clearly a qualified judge of human chess intelligence, declared that he found Deep Blue’s playing skill to be indistinguishable from that of a human chess master during the famous tournament in which he was defeated by Deep Blue. Computers are now displaying human-level intelligence in a growing array of domains, including medical diagnosis, financial investment decisions, the design of products such as jet engines, and a myriad of other tasks that previously required humans to accomplish. We can say that such “narrow AI” is the threshold that the field of AI has currently achieved. However, the subtle and supple skills required to pass the broad Turing test as originally described by Turing is far more difficult than any narrow Turing Test. In my view, there is no set of tricks or simpler algorithms (i.e., methods simpler than those underlying human level intelligence) that would enable a machine to pass a properly designed Turing test without actually possessing intelligence at a fully human level.

There has been a great deal of philosophical discussion and speculation concerning the issue of consciousness, and whether or not we should consider a machine that passed the Turing test to be conscious. Clearly, the Turing test is not an explicit test for consciousness. Rather, it is a test of human-level performance. My own view is that inherently there is no objective test for subjective experience (i.e., consciousness) that does not have philosophical assumptions built into it. The reason for this has to do with the difference between the concepts of objective and subjective experience. However, it is also my view that once nonbiological intelligence does achieve a fully human level of intelligence, such that it can pass the Turing test, humans will treat such entities as if they were conscious. After all, they (the machines) will get mad at us if we don’t. However, this is a political prediction rather than a philosophical position.

It is also important to note that once a computer does achieve a human level of intelligence, it will necessarily soar past it. Electronic circuits are already at least 10 million times faster than the electrochemical information processing in our interneuronal connections. Machines can share knowledge instantly, whereas we biological humans do not have quick downloading ports on our neurotransmitter concentration levels, interneuronal connection patterns, nor any other biological bases of our memory and skill. Language-capable machines will be able to access vast and accurate knowledge bases, including reading and mastering all the literature and sources of information available to our human-machine civilization. Thus “Turing Test level” machines will be able to combine human level intelligence with the powerful ways in which machines already excel. In addition, machines will continue to grow exponentially in their capacity and knowledge. It will be a formidable combination.

Why I Think I Will Win. In considering the question of when machine (i.e., nonbiological) intelligence will match the subtle and supple powers of human biological intelligence, we need to consider two interrelated but distinct questions: when will machines have the hardware capacity to match human information processing, and when will our technology have mastered the methods, i.e., the software of human intelligence. Without the latter, we would end up with extremely fast calculators, and would not achieve the endearing qualities that characterize human discernment (nor the deep knowledge and command of language necessary to pass a full Turing test!).

Both the hardware and software sides of this question are deeply influenced by the exponential nature of information-based technologies. The exponential growth that we see manifest in “Moore’s Law” is far more pervasive than commonly understood. Our first observation is that the shrinking of transistors on an integrated circuit, which is the principle of Moore’s Law, was not the first but the fifth paradigm to provide exponential growth to computing (after electromechanical calculators, relay-based computers, vacuum tube-based computing, and discrete transistors). Each time one approach begins to run out of steam, research efforts intensify to find the next source of renewed exponential growth (e.g., vacuum tubes were made smaller until it was no longer feasible to maintain a vacuum, which led to transistors). Thus the power and price-performance of technologies, particularly information-based technologies, grow as a cascade of S-curves: exponential growth leading to an asymptote, leading to paradigm shift (i.e., innovation), and another S-curve. Moreover, the underlying theory of the exponential growth of information-based technologies, which I call the law of accelerating returns, as well as a detailed examination of the underlying data, show that there is a second level of exponential growth, i.e., the rate of exponential growth is itself growing exponentiallyi.

Second, this phenomenon of ongoing exponential growth through a cascade of S-curves is far broader than computation. We see the same double exponential growth in a wide range of technologies, including communication technologies (wired and wireless), biological technologies (e.g., DNA base-pair sequencing), miniaturization, and of particular importance to the software of intelligence, brain reverse engineering (e.g., brain scanning, neuronal and brain region modeling).

Within the next approximately fifteen years, the current computational paradigm of Moore’s Law will come to an end because by that time the key transistor features will only be a few atoms in width. However, there are already at least two dozen projects devoted to the next (i.e., the sixth) paradigm, which is to compute in three-dimensions. Integrated circuits are dense but flat. We live in a three-dimensional world, our brains are organized in three dimensions, and we will soon be computing in three dimensions. The feasibility of three-dimensional computing has already been demonstrated in several landmark projects, including the particularly powerful approach of nanotube-based electronics. However, for those who are (irrationally) skeptical of the potential for three-dimensional computing, it should be pointed out that achieving even a conservatively high estimate of the information processing capacity of the human brain (i.e., one hundred billion neurons times a thousand connections per neuron times 200 digitally controlled analog “transactions” per second, or about 20 million billion operations per second) will be achieved by conventional silicon circuits prior to 2020.

It is correct to point out that achieving the “software” of human intelligence is the more salient, and more difficult, challenge. On multiple levels, we are being guided in this effort by a grand project to reverse engineer (i.e., understand the principles of operation of) the human brain itself. Just as the human genome project accelerated (with the bulk of the genome being sequenced in the last year of the project), the effort to reverse engineer the human brain is also growing exponentially, and is further along than most people realize. We already have highly detailed mathematical models of several dozen of the several hundred types of neurons found in the brain. The resolution, bandwidth, and price-performance of human brain scanning is also growing exponentially. By combining the neuron modeling and interconnection data obtained from scanning, scientists have already reverse engineered two dozen of the several hundred regions of the brain. Implementations of these reverse engineered models using contemporary computation matches the performance of the biological regions that were recreated in significant detail. Already, we are in a early stage of being able to replace small regions of the brain that have been damaged from disease or disability using neural implants (e.g., ventral posterior nucleus, subthalmic nucleus, and ventral lateral thalamus neural implants to counteract Parkinson’s Disease and tremors from other neurological disorders, cochlear implants, emerging retinal implants, and others).

If we combine the exponential trends in computation, communications, and miniaturization, it is a conservative expectation that we will within 20 to 25 years be able to send tiny scanners the size of blood cells into the brain through the capillaries to observe interneuronal connection data and even neurotransmitter levels from up close. Even without such capillary-based scanning, the contemporary experience of the brain reverse engineering scientists, (e.g., Lloyd Watts, who has modeled over a dozen regions of the human auditory system), is that the connections in a particular region follow distinct patterns, and that it is not necessary to see every connection in order to understand the massively parallel, digital controlled analog algorithms that characterize information processing in each region. The work of Watts and others has demonstrated another important insight, that once the methods in a brain region are understood and implemented using contemporary technology, the computational requirements for the machine implementation requires on the order of a thousand times less computation than the theoretical potential of the biological neurons being simulated.

A careful analysis of the requisite trends shows that we will understand the principles of operation of the human brain and be in a position to recreate its powers in synthetic substrates well within thirty years. The brain is self-organizing, which means that it is created with relatively little innate knowledge. Most of its complexity comes from its own interaction with a complex world. Thus it will be necessary to provide an artificial intelligence with an education just as we do with a natural intelligence. But here the powers of machine intelligence can be brought to bear. Once we are able to master a process in a machine, it can perform its operations at a much faster speed than biological systems. As I mentioned, contemporary electronics is already more than ten million times faster than the human nervous system’s electrochemical information processing. Once an AI masters human basic language skills, it will be in a position to expand its language skills and general knowledge by rapidly reading all human literature and by absorbing the knowledge contained on millions of web sites. Also of great significance will be the ability of machines to share their knowledge instantly.

One challenge to our ability to master the apparent complexity of human intelligence in a machine is whether we are capable of building a system of this complexity without the brittleness that often characterizes very complex engineering systems. This a valid concern, but the answer lies in emulating the ways of nature. The initial design of the human brain is of a complexity that we can already manage. The human brain is characterized by a genome with only 23 million bytes of useful information (that’s what left of the 800 million byte genome when you eliminate all of the redundancies, e.g., the sequence called “ALU” which is repeated hundreds of thousands of times). 23 million bytes is smaller than Microsoft WORD. How is it, then, that the human brain with its 100 trillion connections can result from a genome that is so small? The interconnection data alone is a million times greater than the information in the genome.

The answer is that the genome specifies a set of processes, each of which utilizes chaotic methods (i.e., initial randomness, then self-organization) to increase the amount of information represented. It is known, for example, that the wiring of the interconnections follows a plan that includes a great deal of randomness. As the individual person encounters her environment, the connections and the neurotransmitter level pattern self-organize to better represent the world, but the initial design is specified by a program that is not extreme in its complexity.

Thus we will not program human intelligence link by link as in some massive expert system. Nor is it the case that we will simply set up a single genetic (i.e., evolutionary) algorithm and have intelligence at human levels automatically evolve itself. Rather we will set up an intricate hierarchy of self-organizing systems, based largely on the reverse engineering of the human brain, and then provide for its education. However, this learning process can proceed hundreds if not thousands of times faster than the comparable process for humans.

Another challenge is that the human brain must incorporate some other kind of “stuff” that is inherently impossible to recreate in a machine. Penrose imagines that the intricate tubules in human neurons are capable of quantum based processes, although there is no evidence for this. I would point out that even if the tubules do exhibit quantum effects, there is nothing barring us from applying these same quantum effects in our machines. After all, we routinely use quantum methods in our machines today. The transistor, for example, is based on quantum tunneling. The human brain is made of the same small list of proteins that all biological systems are comprised of. We are rapidly recreating the powers of biological substances and systems, including neurological systems, so there is little basis to expect that the brain relies on some nonengineerable essence for its capabilities. In some theories, this special “stuff” is associated with the issue of consciousness, e.g., the idea of a human soul associated with each person. Although one may take this philosophical position, the effect is to separate consciousness from the performance of the human brain. Thus the absence of such a soul may in theory have a bearing on the issue of consciousness, but would not prevent a nonbiological entity from the performance abilities necessary to pass the Turing test.

Another challenge is that an AI must have a human or human-like body in order to display human-like responses. I agree that a body is important to provide a situated means to interact with the world. The requisite technologies to provide simulated or virtual bodies are also rapidly advancing. Indeed, we already have emerging replacements or augmentations for virtually every system in our body. Moreover, humans will be spending a great deal of time in full immersion virtual reality environments incorporating all of the senses by 2029, so a virtual body will do just as well. Fundamentally, emulating our bodies in real or virtual reality is a less complex task than emulating our brains.

Finally, we have the challenge of emotion, the idea that although machines may very well be able to master the more analytical cognitive abilities of humans, they inherently will never be able to master the decidedly illogical and much harder to characterize attributes of human emotion. A slightly broader way of characterizing this challenge is to pose it in terms of “qualia,” which refers essentially to the full range of subjective experiences. Keep in mind that the Turing test is assessing convincing reactions to emotions and to qualia. The apparent difficulty of responding appropriately to emotion and other qualia appears to be at least a significant part of Mitchell Kapor’s hesitation to accept the idea of a Turing-capable machine. It is my view that understanding and responding appropriately to human emotion is indeed the most complex thing that we do (with other types of qualia being if anything simpler to respond to). It is the cutting edge of human intelligence, and is precisely the heart of the Turing challenge. Although human emotional intelligence is complex, it nonetheless remains a capability of the human brain, with our endocrine system adding only a small measure of additional complexity (and operating at a relatively low bandwidth). All of my observations above pertain to the issue of emotion, because that is the heart of what we are reverse engineering. Thus, we can say that a side benefit of creating Turing-capable machines will be new levels of insight into ourselves.

i All of the points addressed in this statement of “Why I Think I Will Win” (the Long Now Turing Test Wager) are examined in more detail in my essay “The Law of Accelerating Returns” available at /.

Comments (2)

I’ve been reading and rereading this wager and the associated commentaries and a number of thoughts come to mind.

First, it seems that Kurzweil has the harder end of the wager. Mitch Kapor is only betting that the Turing test will not be passed by the end of 2029. He’s not betting that it will never be passed, just not by that date. Kurzweil, however, is betting that it will not only be passed, but by that specific year. Therefore, Kurzweil has to be right on two counts while Kapor only has to be right on one.

I don’t agree with Kurzweil that the most difficult part of passing the test will be that the AI has to invent a believable life story for itself. No one says that it has to invent such a life story on the fly. A very detailed life story could be written for it that will encompass any question that it will likely be asked.

I think the real problem will be inculcating commonsense into the AI. For example, if someone asks it “when were you born? and the AI responds “I was born on December 12, 1993 at 1:15 PM,” this will be a dead giveaway that the tester is conversing with an artificial entity, because real people do not answer a question with that kind of specificity.

A big challenge is that the AI will have to be able to “learn” from the current dialogue. If the tester says that they live in Chicago, And then the AI later in the discussion asks where they live, This will be another red flag.

I think Kurzweil’s most powerful argument is the law of accelerating returns which says that progress advances at an exponential rate. Anybody who understands exponential progress realizes how powerful this is.

However, even assuming that the exponential advancement continues, this says nothing about how many doublings will be required to achieve a particular goal. The power of computers and brain scanning will perhaps be 30,000 times more powerful by the end of 2029 than it is today, but will this be enough? For that matter, will 50,000 times or 100,000 times be enough?

I’m dictating this comment by voice, and despite all the years of doubling of the power of the technology, I’m still appalled by how many mistakes it makes, many, but not all, of which I am correcting. That said, I’m still holding out hope that 2029 will be the big year.

Hi Ray,
I’m very excited to see Watson play jeopardy soon and like you I think you will win the bet with Mitch. Where I break ranks, however, is in suggesting that passing the Turing test per se makes a computer system equivalent to general human intelligence. Instead it could reasonably be argued that it will make a computer equivalent to a functional idiot savant who can answer questions on a variety of topics. Could it, however, philosophize on it’s own and synthesize from disparate blocks of information? I doubt it if the minimum bar of the Turing test is considered. That said, I both believe that the ability to philosophize and synthesize could be designed in rather quickly (less than a decade) after the turing test is passed and secondly that a chatbot agent like Watson with just a handful of improvements is a “good enough” tool for most humans and one with synthetic reasoning skills would make an excellent research assistant.